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Load Packages

library(arrow)
library(tidyverse)
library(ggplot2)
library(ggbeeswarm)
library(dplyr)

Upload Parquet File:

d1 = read_parquet("C:/Users/asare/Downloads/prevalence_by_geography_and_year_and_source (2).parquet")

head(d1)

Add Regions Column:

d1 = d1 %>%
  mutate(region = case_when(
    geography %in% c("Illinois", "Indiana", "Iowa", "Kansas", "Michigan", "Minnesota", 
                 "Missouri", "Nebraska", "North Dakota", "Ohio", "South Dakota", "Wisconsin") ~ "Midwest",
    geography %in% c("Connecticut", "Maine", "Massachusetts", "New Hampshire", "New Jersey", 
                 "New York", "Pennsylvania", "Rhode Island", "Vermont") ~ "Northeast",
    geography %in% c("Alaska", "Arizona", "California", "Colorado", "Hawaii", "Idaho", 
                 "Montana", "Nevada", "New Mexico", "Oregon", "Utah", "Washington", "Wyoming") ~ "West",
    geography %in% c("Alabama", "Arkansas", "Delaware", "District of Columbia", "Florida", "Georgia", 
                 "Kentucky", "Louisiana", "Maryland", "Mississippi", "North Carolina", "Oklahoma", 
                 "South Carolina", "Tennessee", "Texas", "Virginia", "West Virginia") ~ "South",
    TRUE ~ "Other"
  ))

#Separate Datasets by United States only and States Only

state = d1 %>% filter(! region %in% c("Other"))
us = d1 %>% filter(region == "Other")

Create Separate Region Only Dataset

Midwest = c("Illinois", "Indiana", "Iowa", "Kansas", "Michigan", "Minnesota", 
                 "Missouri", "Nebraska", "North Dakota", "Ohio", "South Dakota", "Wisconsin") 
Northeast = c("Connecticut", "Maine", "Massachusetts", "New Hampshire", "New Jersey", 
                 "New York", "Pennsylvania", "Rhode Island", "Vermont") 
West = c("Alaska", "Arizona", "California", "Colorado", "Hawaii", "Idaho", 
                 "Montana", "Nevada", "New Mexico", "Oregon", "Utah", "Washington", "Wyoming")
South = c("Alabama", "Arkansas", "Delaware", "District of Columbia", "Florida", "Georgia", 
                 "Kentucky", "Louisiana", "Maryland", "Mississippi", "North Carolina", "Oklahoma", 
                 "South Carolina", "Tennessee", "Texas", "Virginia", "West Virginia")

Midwest <- c("Illinois", "Indiana", "Iowa", "Kansas", "Michigan", "Minnesota",
             "Missouri", "Nebraska", "North Dakota", "Ohio", "South Dakota", "Wisconsin")

Northeast <- c("Connecticut", "Maine", "Massachusetts", "New Hampshire", "New Jersey",
               "New York", "Pennsylvania", "Rhode Island", "Vermont")

West <- c("Alaska", "Arizona", "California", "Colorado", "Hawaii", "Idaho",
          "Montana", "Nevada", "New Mexico", "Oregon", "Utah", "Washington", "Wyoming")

South <- c("Alabama", "Arkansas", "Delaware", "District of Columbia", "Florida", "Georgia",
           "Kentucky", "Louisiana", "Maryland", "Mississippi", "North Carolina", "Oklahoma",
           "South Carolina", "Tennessee", "Texas", "Virginia", "West Virginia")

region_df <- data.frame(
  state = c(Midwest, Northeast, West, South),
  region = c(rep("Midwest", length(Midwest)),
             rep("Northeast", length(Northeast)),
             rep("West", length(West)),
             rep("South", length(South)))
)

Create Map

library(geofacet)

#install.packages("choroplethr")
#library(choroplethr)
#install.packages("plotly")
library(plotly)

state_choropleth(region_df, geoid.name = 'state', value.name = 'region')

ggsave("color_coded_map_region.png", width = 10, height = 10)

Look at Total Regional Prevalence for Obesity and Diabetes

fig1 = ggplot(state %>% filter(year == "2024", age == "Total", outcome_name == "Diabetes"), aes(x  = region, y = value, color = geography, group = region)) +  
  geom_quasirandom(dodge.width = 0.8, size = 2.5, alpha = 0.8)  +
  facet_wrap(~source) +
  labs(
    title = "Total 2024 Diabetes Prevalence by Region",
    subtitle = "CDC BRFSS, Epic Cosmos: HbA1c, Epic Cosmos: ICD10 Data",
    x = "Region",
    y = "Prevalence (%)",
    color = "Region:"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    axis.text.x = element_text(size = 16, angle = 45, hjust = 1),
    plot.title = element_text(size = 20, hjust = 0.5, face = "bold"),
    plot.subtitle = element_text(size = 14, hjust = 0.5), panel.spacing = unit(3.5, "lines"), strip.text = element_text(size = 15, face = "bold"), legend.position = "none"
  )


fig1 = ggplotly(fig1, tooltip = c("geography", "value"))
fig1

# Save ggplotly as widget in file test.html
saveWidget(ggplotly(fig1), file = "diab_int.html")

test_df = state %>% filter(year == "2024", age == "Total", outcome_name == "Obesity", region == "South") %>% select(geography, region, value, source)
write.csv(test_df , "mydata.csv", row.names = FALSE)

ggplot(state %>%
         filter(year != "2025",
                age == "Total",
                outcome_name == "Diabetes",
                source != "Medicare FFS"),
       aes(x = year, y = value, color = geography, group = geography)) +
  geom_line() +
  facet_wrap(~ region) +
  labs(
    title = "Total 2024 Diabetes Prevalence by Region",
    subtitle = "CDC BRFSS, Epic Cosmos: HbA1c, Epic Cosmos: ICD10 Data",
    x = "Year",
    y = "Prevalence (%)",
    color = "State"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    axis.text.x = element_text(size = 16, angle = 45, hjust = 1),
    plot.title = element_text(size = 20, hjust = 0.5, face = "bold"),
    plot.subtitle = element_text(size = 16, hjust = 0.5),
    panel.spacing = unit(3.5, "lines"),
    strip.text = element_text(size = 16, face = "bold")
  )